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Record W2117352458 · doi:10.1109/csee.2002.995194

Learner-centered software engineering education: from resources to skills and pedagogical patterns

2003· article· en· W2117352458 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicModel-Driven Software Engineering Techniques
Canadian institutionsConcordia University
Fundersnot available
KeywordsRetrainingCurriculumBachelorContext (archaeology)Computer scienceSoftware engineeringEngineering managementCDIOEngineering educationEngineeringPedagogyPsychology

Abstract

fetched live from OpenAlex

A revolution is taking place in academic and continuing education, one that deals with the philosophy of how we teach and learn, the relationship between educators and learners, the way in which the classroom is structured, and the nature of the curriculum. This new approach, termed learner-centered education, is focused on the needs, skills and interests of the learner rather than on the organization of curriculum content. This paper describes an approach for identifying critical skills and for designing training material for learner-centered software engineering education. The approach starts from an analysis of the software developer's context of work, identifies critical skills and then associates relevant learning resources with them. The approach has been successfully used and validated in a real world-training program called PRISE that the first author developed-Programme de Reorientation des Ingenieurs Sans Emploi, a Curriculum for Retraining Unemployed Engineers in Software Engineering. The approach is also being used in some courses in the Concordia bachelor of software engineering program.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.840
Threshold uncertainty score0.672

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.028
GPT teacher head0.274
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it